Lipid-metabolic risk factors of chronic non-communicable diseases among patients with gastroesophageal reflux disease
Bibliographic record
Abstract
Lipid and metabolic risk factors for chronic noninfectious diseases for Yakuts with gastroesophageal reflux disease (GERD) and metabolic syndrome (MS) assessment work was carried out. A cross-sectional study of 140 patients with GERD was done. Depending on the MS availability and ethnicity, patients were divided into 3 groups. The main group included 50 Yakut nationality patients with GERD and MS. The comparison group I consisted of 50 Yakuts with GERD and without MS, the comparison group II consisted of 40 Russian GERD and MS. Preliminary verification of the diagnosis of GERD was done according to the Mayo Clinic and the Montreal Consensus (2006). The components of MS were determined on the basis of the recommendations of the All-Russian Scientific Society of Cardiology from 2009. Statistical processing and analysis of data was performed using the package IBM SPSS Statistics 19. Pair comparison was performed using the Mann-Whitney test. To assess the association of clinical symptoms of GERD with components of MS was used binary logistic regression method with forced inclusion of predictors. Analysis of lipid metabolic risk factors for chronic noninfectious diseases for Yakuts with gastroesophageal reflux disease and metabolic syndrome showed that sistolic and diastolic blood pressure, total cholesterol, LDL, triglycerides and the atherosclerotic index in patients with GERD and MS are characterized by high rates during normal value high density lipoprotein cholesterol. Triglyceride levels in Yakuts with MS are above recommended values, but at the same time as compared to the same period at the Russian MS were significantly lower. Assessment of the relationship of clinical symptoms of GERD with the criteria of MS in people of Yakut nationality showed the contribution of components of MS, especially abdominal obesity, high blood pressure and triglycerides, in the development of dyspeptic symptoms (bloating, heaviness in the epigastric), esophageal (epigastric burning) and extraesophageal symptoms (nocturnal cough) of GERD .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".